Results 81 to 90 of about 648,662 (306)
Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining [PDF]
An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms.
Alexandru PIRJAN
core
ANALISIS DAN IMPLEMENTASI ALGORITMA SQL BASED FREQUENT PATTERN MINING DENGAN FREQUENT PATTERN – GROWTH (FP-GROWTH) [PDF]
, 2006 ABSTRAKSI: Scalable data mining dalam database yang berukuran besar saat ini merupakan tantangan pada penelitian database. Integrasi data mining dengan database system merupakan komponen yang sangat penting untuk aplikasi data mining dengan ukuran yang ...
M.Farhad Al asad
core
KIPS Transactions on Software and Data Engineering, 2016 현존하는 빈발 패턴 마이닝 방법은 대부분 시간 효율성을 목표로 하고, 물리적 메모리 사용에 매우 의존적이다. 하지만 빅데이터 시대가 도래함에 따라 실제 세상의 데이터베이스는 급속도로 증가하고 있으며, 그에 따라 기존의 방법으로 현실적인 거대한 양의 데이터를 마이닝하기에 물리적 메모리 공간이 부족한 실정이다. 이러한 문제를 해결하기 위해, 빈발 패턴 마이닝의 메모리 의존성을 줄이기 위한 보조저장장치 기반의 연구들이 진행되었으나, 메모리 기반의 방법들에 비해 처리 시간이 너무 많이 소비된다는 한계가 있었다.
Jung-Hun Lee, Youn-A Min
openaire +2 more sources
Reciprocal control of viral infection and phosphoinositide dynamics
FEBS Letters, EarlyView.Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley +1 more source
In pursuit of efficient frequent pattern mining in data streams
, 2017 碩士資料串流探勘(Data stream mining)是現今大數據(Big data)領域的研究主題之一。資料串流指的是日常生活中隨著時間不斷產生的大量資料。例如：每個人上網的點擊串流、感測器產生的資料、購物資料、網路流量資料…等。這些資料串流中隱含著許多有價值的資訊，利用資料探勘(Data mining)的技術，可以分析出針對不同應用之下所需要的結果，最後做出更佳的決策。在資料探勘的領域中，其中有一個主題是頻繁樣式探勘(Frequent pattern mining ...
凃耘昇;Tu, Yun-Sheng
core
Mining Frequent Patterns in Evolving Graphs
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018 Given a labeled graph, the frequent-subgraph mining (FSM) problem asks to find all the $k$-vertex subgraphs that appear with frequency greater than a given threshold. FSM has numerous applications ranging from biology to network science, as it provides a compact summary of the characteristics of the graph. However, the task is challenging, even more so
Nasir, Muhammad Anis Uddin +4 more
openaire +7 more sources
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source
Mining frequent trajectory pattern based on vague space partition
, 2013 Frequent trajectory pattern mining is an important spatiotemporal data mining problem with broad applications. However, it is also a difficult problem due to the approximate nature of spatial trajectory locations.
Wang L(王亮) +3 more
core
New approaches to weighted frequent pattern mining [PDF]
, 2007 Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns.
Yun, Unil
core
Enabling Far-Edge Analytics: Performance Profiling of Frequent Pattern Mining Algorithms
IEEE Access, 2017 Far-edge analytics refers to the enablement of data mining algorithms in far-edge mobile devices that are part of mobile edge cloud computing (MECC) systems.
Khubaib Amjad Alam +2 more
doaj +1 more source